app.py

import gradio as gr
import openai
from langchain.chains import RetrievalQA
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
import os

OPENAI_API_KEYS = os.getenv("OPENAI_API_KEYS")

# Knowledge base for Crustdata APIs
docs = """
# Crustdata Dataset API

## Description
The Crustdata Dataset API provides access to a wide variety of datasets across different domains. It allows users to search, filter, and retrieve datasets based on categories, tags, and other metadata.

## Key Endpoints

### 1. **GET /datasets**
- **Description**: Retrieves a list of available datasets.
- **Parameters**:
  - `category` (optional): Filter datasets by a specific category.
  - `tags` (optional): Filter datasets by tags (comma-separated).
  - `limit` (optional): Maximum number of datasets to return (default: 10).

- **Example Request**:
  ```bash
  curl -X GET "https://api.crustdata.com/datasets?category=finance&tags=economy,stocks&limit=5"
  ```

- **Example Response**:
  ```json
  {
    "datasets": [
      {
        "id": "12345",
        "name": "Global Finance Dataset",
        "category": "finance",
        "tags": ["economy", "stocks"]
      },
      ...
    ]
  }
  ```

### 2. **GET /datasets/{id}**
- **Description**: Retrieves detailed information about a specific dataset.
- **Parameters**:
  - `id` (required): The unique identifier of the dataset.

- **Example Request**:
  ```bash
  curl -X GET "https://api.crustdata.com/datasets/12345"
  ```

- **Example Response**:
  ```json
  {
    "id": "12345",
    "name": "Global Finance Dataset",
    "description": "A comprehensive dataset on global financial markets.",
    "category": "finance",
    "tags": ["economy", "stocks"],
    "source": "World Bank"
  }
  ```

---

# Crustdata Discovery and Enrichment API

## Description
The Crustdata Discovery and Enrichment API allows users to enrich their datasets by adding metadata, geolocation information, and other relevant attributes.

## Key Endpoints

### 1. **POST /enrich**
- **Description**: Enriches input data with additional metadata based on the specified enrichment type.
- **Parameters**:
  - `input_data` (required): A list of data entries to be enriched.
  - `enrichment_type` (required): The type of enrichment to apply. Supported types:
    - `geolocation`
    - `demographics`

- **Example Request**:
  ```bash
  curl -X POST "https://api.crustdata.com/enrich" \
    -H "Content-Type: application/json" \
    -d '{
          "input_data": [{"address": "123 Main St, Springfield"}],
          "enrichment_type": "geolocation"
        }'
  ```

- **Example Response**:
  ```json
  {
    "enriched_data": [
      {
        "address": "123 Main St, Springfield",
        "latitude": 37.12345,
        "longitude": -93.12345
      }
    ]
  }
  ```

### 2. **POST /search**
- **Description**: Searches for relevant metadata or datasets based on user-provided criteria.
- **Parameters**:
  - `query` (required): The search term or query string.
  - `filters` (optional): Additional filters to narrow down the search results.
  
- **Example Request**:
  ```bash
  curl -X POST "https://api.crustdata.com/search" \
    -H "Content-Type: application/json" \
    -d '{
          "query": "energy consumption",
          "filters": {"category": "energy"}
        }'
  ```

- **Example Response**:
  ```json
  {
    "results": [
      {
        "id": "67890",
        "name": "Energy Consumption Dataset",
        "category": "energy",
        "tags": ["consumption", "renewables"]
      }
    ]
  }
  ```

---

# General Notes
- All endpoints require authentication using an API key.
- API requests must include the `Authorization` header:
  ```plaintext
  Authorization: Bearer YOUR_API_KEY
  ```
- Response format: JSON
- Base URL: `https://api.crustdata.com`
"""

# Split the documentation into chunks for embedding
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
doc_chunks = text_splitter.create_documents([docs])

# Embed the documents using OpenAI embeddings
embeddings = OpenAIEmbeddings()
docsearch = FAISS.from_documents(doc_chunks, embeddings)

# Create a QA chain
qa_chain = RetrievalQA.from_chain_type(
    llm=OpenAI(model="gpt-3.5-turbo"),
    retriever=docsearch.as_retriever(),
    return_source_documents=True
)

# Function to handle user queries
def answer_question(question):
    result = qa_chain.run(question)
    return result

# Create a Gradio interface
chat_interface = gr.Interface(
    fn=answer_question,
    inputs=gr.Textbox(lines=2, placeholder="Ask a question about Crustdata APIs..."),
    outputs="text",
    title="Crustdata API Chat",
    description="Ask any technical questions about Crustdata’s Dataset and Discovery APIs.",
)

# Launch the Gradio app
chat_interface.launch(share=True)